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Closed-set automatic speaker identification using multi-scale recurrent networks in non-native children.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management·2023
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Audio Augmentation for Non-Native Children's Speech Recognition through Discriminative Learning.

Kodali Radha1, Mohan Bansal1

  • 1School of Electronics Engineering, VIT-AP University, Amaravati 522237, India.

Entropy (Basel, Switzerland)
|July 8, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances automatic speech recognition (ASR) for non-native children by developing feature-space discriminative models. Speed perturbation data augmentation significantly improves ASR performance for children acquiring a second language.

Keywords:
data augmentationdiscriminative modelsmutual informationnon-native children speech recognitionspeed perturbation

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Area of Science:

  • Human-computer interaction
  • Speech processing
  • Second language acquisition

Background:

  • Children's increasing interaction with virtual assistants drives advancements in automatic speech recognition (ASR).
  • Non-native children exhibit unique speech errors during second language (L2) acquisition, challenging current ASR systems.
  • Existing ASR struggles to accurately recognize the speech patterns of non-native children.

Purpose of the Study:

  • To develop an effective automatic speech recognition system for non-native children.
  • To improve ASR performance by addressing L2 acquisition speech characteristics.
  • To investigate the impact of L2 proficiency on ASR systems for children.

Main Methods:

  • Utilized feature-space discriminative models, including feature-space maximum mutual information (fMMI) and boosted feature-space maximum mutual information (fbMMI).
  • Implemented speed perturbation-based data augmentation on children's speech corpora.
  • Collected and analyzed speech data encompassing various speaking styles, including read and spontaneous speech.

Main Results:

  • Feature-space MMI models demonstrated superior performance compared to traditional ASR baseline models.
  • Increasing speed perturbation factors positively correlated with improved ASR accuracy.
  • The developed system showed enhanced recognition capabilities for non-native children's speech.

Conclusions:

  • Feature-space discriminative models combined with speed perturbation offer a promising approach for non-native children's ASR.
  • Addressing specific challenges in L2 acquisition speech is crucial for robust ASR systems.
  • This research contributes to more inclusive and effective human-computer interaction technologies for young learners.